Questions related to Land Use Change
I am using Google Earth Engine for LULC classification map. For this purpose I have used smile random forest classifier to classify the Landsat 7 Top of Atmosphere data. Now could you please tell me how can I validate the LULC classification map?
A New Frontiers Journal is to be launched soon: Frontiers in Earth Observation and Land Monitoring (Cf attached Author Guide)
Frontiers in Earth Observation and Land Monitoring is a multidisciplinary open-access journal that presents major advances in the monitoring and understanding of key land surface processes and in the optimal use of different observation systems dedicated to land monitoring.
We are working towards launching new journals with a foundation of quality content to generate as much interest as possible from the community. To do this, we appoint only the best researchers in their areas of expertise and commission at least 10 articles before launch, which we'll make available through an early-access page on our website.
We do not want our fees to be a barrier to publishing quality research, therefore we have several author support services available. To find out more, contact us at email@example.com
I have just learned Dyna_CLUE modeling to simulate the future land use maps. If someone having stock with it kindly contact me, i have prepare easy guidelines to learn it. There are few crucial steps which you can not learn from user manual. Thanks
ارزیابی تأثیر تغییرات کاربری اراضی ناشی از تغییراقلیم بر فرونشست زمین در مخروط افکنه ها و دشت ها بعنوان مهمترین کانون سکونتگاهی روستایی و کلان شهرها در ایران با استفاده از تکنیکهای دورسنجی و میدانی ممکن است. امروزه، ماهیت پویای تغییرات اقلیمی و پیامدهای مرتبط با آن، چالشهای جدی را برای دشت ها و مخروط افکنه ها ومحیطهای شهری به ویژه با تغییرات قابل توجه کاربری زمین در حوضه های بالادستی ایجاد کرده است. این تغییرات می تواند تأثیرات عمیقی بر فرونشست زمین داشته باشد که نیاز به بررسی دقیق در آمایش و برنامه ریزی و مدیریت سکونتگاه های صنعتی ، روستایی و شهری دارد.
این پژوهش با استفاده از دادههای اقلیمی، زمین شناختی، دورسنجی و میدانی و مدلسازی ها آماری، رابطه بین تغییرات کاربری اراضی ناشی از تغییراقلیم و فرونشست زمین را بررسی میکند. این مطالعه الگوهای مکانی-زمانی تغییرات کاربری زمین را بررسی میکند و مناطقی فرونشستی را برجسته میکند که تغییرات قابلتوجهی را به دلیل تأثیرات تغییر اقلیم تجربه کرده و خواهد کرد.
نتایج این تحقیق به درک جامعی از تأثیر متقابل پیچیده بین تغییرات کاربری اراضی ناشی از تغییرات اقلیم و فرونشست زمین در دشت ها و مناطق شهری کمک خواهد کرد. با کمی کردن نرخ و میزان فرونشست زمین مرتبط با این تغییرات، برنامه ریزان و مدیران شهری می توانند استراتژی های موثری برای کاهش اثرات نامطلوب و تقویت توسعه پایدار ایجاد کنند.
پیامدهای این مطالعه به برنامهریزی و شیوههای مدیریت سکونتگاههی (در مقیاس های متفاوت؛ ملی، استانی)هم در دشتها و هم در مناطق سکونتگاههاس روستایی-شهری گسترش مییابد. بینشهای بهدستآمده از ارزیابی مبتنی بر دورسنجی و مدلسازی به شناسایی مناطق آسیبپذیر و تدوین تدابیر انطباق و کاهش هدفمند کمک خواهد کرد. نتایج تحقیق میتواند سیاستهای کاربری زمین، برنامهریزی زیرساخت، و استراتژیهای کاهش خطر بلایا .را قابل پیس بینی کرده و در نتیجه انعطافپذیری و پایداری محیطهای شهری را ممکن و افزایش ده.
سوال مهم در این پژوهش این است که؛عناصر اساسی موثر در "بررسی و ارزیابی تاثیرات تغییرات اقلیمی و کاربری زمین برفرونشست زمین در دشت ها، مخروط افکنه ها و کلان شهرها" کدامند؟
Can someone recommend a model (preferably not too input demanding) that simulates climate change and/or land use-change scenario using Ecosystem services as output (e.g., soil c stock, primary production, landscape aesthetic value, ...)
I want to know any available article of statistical method to estimate - Human activities (Anthropogenic), which can accelerate snow melting. Please help to find any suitable method to any published article on this topic.
There are several human activities (GHGs emission, CO2 release, urbanization etc.), which resulted massive snow melting now a days. But to quantify the percentage (%) of share coming from Human activities, which causing SCA change.
Thanks in advance.
Email - firstname.lastname@example.org
If possible How Ecological restoration could be related to Climate Change OR Land Use Change ?
I am doing LULC of the arid region, I have acquired landsate 8 image data from USGS website and I have done preprocess in qgis using semi-automatic classification plugin using the standard tutorial and I have converted by DN into reflections value for LULC. however, I facing difficulties in assigning classes for built-up area and bare soil as they have high overlap spectral values.
besides this, I have also used SAVI as well as Modified bare soil index though it's not helping in my problem.
Anyone can tell me what to do in that case.
Are there any examples of good practices and new paradigms that you come across when considering agricultural land use (especially the measures taken by developing countries)?
In statistics, Cramér's V is a measure of association between two nominal variables, giving a value between 0 and 1 (inclusive). It was first proposed by Harald Cramér (1946).
It is actually considered in many papers I came accross that a threshold value of 0.15 (sometimes even 0.1) can be considered as meaningful, hence giving hints of a low association between the variables being tested. Do you have any reference, mathematical foundation or explanation on why this threshold is relevant ?
What is the best method and resource to create a detailed landcover map of an urban area?
i need these classes: green space, water-body, farmland, bare land, building, and road
I need the land cover maps of 2000, 2005, 2010, 2015, and 2020.
I have tried to give input LULC maps into the model of a sub-basin with irregular boundary. I have faced an issue with no-data pixels. But, when I gave a regular rectangular boundary, there was smooth processing. My question is, Is it mandatory to use a rectangular bounding box for LULC input maps for future prediction models?
Oscar Borsato and I are organizing a free web platform “Urban Policies” where we can discuss by virtual communications of the following topics:
1) Ecologically-Compatible Urban Planning
2) Land Use Changes
3) Modelling Ecosystem Services for sustainable urban planning
We can organize Theoretical, Practical GIS, or Case of Study dicussions.
If you are interested, please let us know (writing at email@example.com or wia WTZ at +39 349 64 26 511) whether we can keep your contact to organize the first online course.
We will create a mailing list and contact of interested researchers.
I have to implement Green Ampt Infiltration equation for daily time step with regional scale over different Land use/Land cover conditions. The Green Ampt parameters are estimated using soil properties, how the equation could be improved for different LULC conditions and what could be the effect of varying spatial and temporal resolution in model performance.
I face a problem in LULC classification, like as an industrial area showing as a water body.
Please help me.
We want to prepare village level crop maps (based on the land use maps) using Google Earth Engine and machine learning algorithm. There are around 27 major crops in Maharashtra.
- Can there be as many as 27 classes in the classification?
- On what basis can we decide the maximum number of classes algorithm can support?
- What is the optimum number of classes to achieve maximum accuracy?
- What is the number of ground truth points required for each class? What sample size is good sample size?
I want to simulate the urban expansion using different time series LULC based on satellite image. please suggest me most suitable model for urban simulation.
Thanks and regards.
I am working on the master plan for a small city in Iran. I have faced a paradoxical situation!
The city is surrounded by green spaces (specifically the garden city or second house spaces). We don't know how to deal with these spaces. If we add them to the formal boundary of the city, they would be the case for converting to housing and other urban land uses. If we put it out of the formal boundary, there would be no control over the transformations.
I would be happy to hear your perspectives on this issue.
I am seeking the best current methods and datasets (highest possible resolution) for defining and assessing global land degradation - ideally with a time series. I know there are different ways of exploring this e.g. biomass, productivity, land use/cover etc., but I would appreciate any thoughts on current modelling, datasets/resources and novel approaches.
I am also interested in the best methods for quantitatively mapping/modelling land restoration (biophysical) on a global scale, and if possible, historic land reconstruction.
I have already been read many articles and found some methods but those are not cleared to me.
Can you please suggest me how to generate different LST map of each land classes using ArcGIS?
Or if you have any other methods to generate the LST of each land class please do recommend?
I have using satellite image of Landsat 8 and 5 for LULC classification. I am confused that which software and method are the best for LULC classification?
Please help me.
Thanks and Regards
I'm looking for a standard model to quantify the land change drivers (e.g., forest to agriculture or vs). Actually, it depends on the data that I have and the specific location, but I would like to have a good review of what has been done on this topic.
This figure is about "Percentage change in land use land cover classes over Africa 2000–2015."
It is collected from a paper titled "Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing"
Can anyone suggest link of any supplementary script relevant to this?
I am doing wrf simulation to study is there any impact of LULC on extreme precipitation. So I need to use Bhuvan's latest LULC data.
I do not have enough funding to purchase software specifically designed for image processing like ENVI and ERDAS Imagine. Alternatively I am seeking a way to make use my ArcMap 10.1, even though I never tried using this for atmospheric correction.
We live in a changing world. Not only the climate is changing but also the system that is exposed to this climate.
In the discussion on the impacts of climate change, the changing climate is given a very important role, while non-climatic factors (e.g. land use, demographic/socio-economic factors) are often only the subject of studies of the status quo. However, the changes or transformation of such non-climatic factors in the future are often not adequately considered.
Aren't these factors equally or even more important, as there is a large scope for action, especially in the distribution of these elements?
Different transportation, land-use, environmental, and other corresponding planners propose their work for new city urban plans in order to address the existing problems by identifying the major gaps they had. The existing land-use was following bad planning principles and it is already a failed plan. When planners integrate their proposed spatial plan to existing land use, they will have a real challenge in aligning. So, as a planner, what would be your choice to align the proposal you have with the existing land-use?
- Shall i follow the standard plan i have and remove the existing unplanned land-uses( NB: the compensation cost may be too large) ? or
- Shall i accept the existing situation as it is and compromise the planning standards?
What is your suggestion?
Any help is appreciated very much.
I am trying to make LULC of an area to access temporal changes using Landsat imageries of TM and OLI sensors. The problem is that the bare area, sand in river body, and built-up area appear same. So, are there any specific indices or speciality of any band that can be used to differentiate these LULC classes?
Dear experts, I badly need a help to solve the problem of Landsat image classification of a coastal region of Bangladesh. The problem is regarding supervised classification of a coastal district. The land cover spectral reflectance value of pixels is quite complex. The Pixel values of Build up area and Barren land are very close. When selecting the Training sample pixels for Urban area and Barren lands that are often in conflict in the output. There are many unwanted areas in the classified image. Like many unwanted buildup areas or many Barren lands occupy Buildup areas. I tried more and less training inputs for urban area several times. but the results are not appropriate. Can you please suggest any ways to solve the problem??
It all started with the Normalized Difference Vegetation Index (NDVI). I am curious to know how a researcher gets to derive or modify such mathematical (sometimes complicated equations) equations by making use of two bands (absorbing and reflecting bands)? Is it by trial and error method?
For example, NDVI seems to be a simple normalization of NIR and RED bands. MSAVI has NIR and red bands along with mathematical operations both in numerator and denominator. How do we come to such a relatively complex formula?
Thank you very much in advance.
I want to predict land-use changes for the distant future (like 2050 or beyond). my data collection is for 1995-2005-2015. So:
1- How to predict for the future? Is creating a model based on for example 1995 to 2015 and predicting based on 2015 information suffice? (and then maybe predicting based on the result of 2015 prediction). Is this type of stepwise prediction the only way?
2- Is there any research paper you could suggest that predicts landuse change for distant future using machine learning techniques?
I have done LULC classification using LANDSAT images for my study area which includes a part of coastal seashore. The classification was done using 2 methods: the conventional supervised MLC method and spectral indices based classification. In both the cases it is found that intermixing of built-up, barren and coastal shore classes. Coastal shore appeared to be built-up class and barren to be again built-up class. Though it is claimed by many of the researchers that spectral indices based classification gave good overall accuracy and Kappa coefficient, the problem mentioned above still persist. It is observed in my current work that overall accuracy and kappa coefficient were enhanced to an extent. But, the problem of intermixing of pixels is more prevalent especially in coastal area.
Please let me know if you have any idea on how to tackle this issue.
I am studying about Remote Sensing, especially the application of remote sensing in land use land cover classification. I have a little bit of experience in python programing and am looking for a good resource for learning Python for image classification of various image data. Can anyone give me such a good resource, please? Thanks in advance.
I have tried TerrSet but it didn't work properly, as It mainly works on urban sprawl. and my study area is free from anthropogenic activities like construction.
Hi, My study covers 1981 to 2018. I need to classify Landsat images of 1981, 1991, 2001, 2011, 2018 to get land cover. I can do accuracy assessment of 2018 classification by ground truth points. but how can i measure the accuracy of 2001 or 1991 year images?
It can be lumped to a catchment level (with average rainfall and % of land cover types e.g. forest, agriculture, urban) or raster cell based. I agree that, there are many other factors such as topography, soil type, evaporation/evapotranspiration, distance to reach as well as daily/event rainfall distribution that influences the river discharge. But at the moment, the purpose is to compare the net change in river discharge (or runoff) in relation to change in rainfall and/or land use land cover only without needing to specify other information.
I tried the Soil curve number method using land cover and hydrologic soil group, but found it not suitable for monthly or annual data as the response for various amount of rainfall differ a lot and it is aimed for event level runoff estimation.
Thanks a lot for the help
Dear friends and colleagues,
our land use maps, which they include five land use classes, have been developed for three time periods. Now, the question is that, are we allowed to use non-parametric test in that case where we just have the area for these land-use classes in three years without repetition?
I wonder if you let me know what action should I take in this regards.
I have created the soil user file and now working of LULC file. I have done almost all only remaining are following:
Both belongs to urban landcover, so its code must come from Urban database and accordingly code ID will be assigned in the user define Land-use text file. AM I right?
Secondly, Desert, Dryland and Plaindryland I am still struggling how to replace them from the land cover closest category.
Could you please guide me how to fix these problems.
Thanks in anticipation.
This project is looking for soil organic carbon data and changes in China.
We are seeking your support in collecting SOC data to predict and map SOC. What we would need are the SOC, bulk density, and soil texture contents for entire China or any provinces or any district in China.
Any metadata in more detail are welcomed.
Thanks in advance.
The 10th IALE World Congress will take place July 1st-5th 2019 in Milan, featuring the theme of "Nature and society facing the Anthropocene challenges and perspectives for landscape ecology". http://www.iale2019.unimib.it/
Have a look at our symposia SYMP4 Reconstructing the past landscapes to simulate future sustainable scenarios through multidisciplinary approaches
We accept abstracts by 25th of February!!
For those of you which had a previous experience with Idridsi TerrSet (from ClarkLabs), I am considering working with LCM (Land Change Modeler) to model land use change over a rural watershed of 40 sq km in the sahelian climate (west africa).
I am considering three different types of land uses. I have the strong belief that all the land uses are being changed under the influence of the same set of driving variables. Amongst them, I have cultivated areas, which are continiously increasing, based on land use demand, which is closely (I believe) related to climate and population size.
Say I have annual population census (or density) values, as well as cumulative annual rainfall values over the whole simulation period. However, these values does not change spatially over the whole area,. They are rather constant in space, but time varying.
How can I input them in the modelling process, using a Multilayer Perceptron (MLP) ?
As part of my research, I am modelling runoff discharge on a rural watershed. I would like to activate Land Use Update with SWAT. I am using ArcGIS 10.3.1 with ArcSWAT, along with SWATCUP 5.1.6 for automatic calibration. I used SWAT LUU tool (see https://saraswat-swat.rcac.purdue.edu/swatluu) to prepare inputs lup.dat and relevant files for 3 differents land use scenerios (2004, 2009 and 2017). My simulation goes from 2004 to 2017, with one year warm up (2004).
I noticed that when I run SWAT-CUP, it seems to find lup.dat, since deleting any of the associated *.dat files containing HRU fractions (HRU_FR) values returns an error. However, during the auto-calibration process, *.hru files are constantly edited, but their HRU_FR (first line) does not change. As such, it seems that land use update does not work.
Are there any recommended guidelines or anything I have missed ? In case any information is needed upon this, I would be glad to provided them. Thanks in advance.
I want to see the effect of LULC change on rainfall over the Indian region. So, want to know how we can replace the default urban LULC with crop land LULC in WRF. Please some one suggest me how to do that in WRF? Presently I am using MODIS data.
Can anyone please help me to find some research papers related to 'solving land use conflicts of an agricultural farm'?
Where to find free or cheap very high resolution multispactral imagery and LIDAR for the entire world ?
Also, repositories of historical imagery/maps would be very useful.
Most land (38.6%) modified by humans for agriculture (pasture + croplands) is about 19.4 million square miles. Another 14.9% (about 7.7 million square miles) is land that is modified for other uses (logging, mines, planted forests, erosion, urban areas). If the urban areas constitute about the 3% of these 7.5 mill. square miles then 58.275 Ha is the land occupied by urban areas in the world. How could I estimate the number of hectares that are used in urban agriculture in the world? Would FAO have this information? Where?
I have a series of land use maps at different times as a result of a classification process on Landsat images. I am mapping 3 different thematic classes : bare soils, vegetation and croplands.
I would like to answer the following : are the trends of differents land use classes significant ? I believe chi-square test might help here.
How should I run the test, step by step ? should i sample some pixels ? how much ? or work with the entire rasters ? Can anyone explain or at least point me to relevant lectures ?
I am going to estimate land surface temperature in a cloudy city and prepare its LST map for further analysis. I searched among Landsat images of the case study , but, the cloud coverage is very high ! (Since I prefer to use high resolution images, i searched among Landsat images).
I would be thankful if you provide me some solutions and suggestions.
I want to relate field measured TSS and reflectance to come up with a regression model to estimate sediment yield.
I want to understand how transitional probabilities are calculated for land use change modelling, given four (4) state land use types for a previous period (T1) and a new period (T2) spanning ten years.
Please, using the above percentages how do I derive the transitional probabilities for the two periods. Make the answer simple.
Thanks for your assistance.
I am working on a research proposal on the issue of agricultural expansion, intensification and deforestation in tropical Latin America. I am particularly interested in looking not only at socio-economic and technological drivers, but also institutional ones. In particular, which aspects of governance are important in preventing further spatial expansion of agriculture in tropical Latin America? How do different land tenure systems affect the process? What is the role of indigenous communities? So far I have been thinking of looking at the dry Chaco in Northern Argentina and perhaps another case in Ecuador (in order to perform a comparative analysis). I would very much appreciate collaborations with local institutions/researchers.
I am looking for a kind member of the Society for Historical Archaeology who could nominate my PhD dissertation to the 2018 Kathleen Kirk Gilmore Dissertation Award, please.
This work is about late-Holocene human occupations and disturbances in Central Africa, their impact on tropical forests and the disruption on land use caused by the European colonization. The manuscript is written in English. It has been well received by the jury (in Belgium) as a outstanding multidisciplinary contribution, at the crossroads between archaeology and ecology.
Please find the text in attachment to have a look on it, and do not hesitate to recommend this post or to forward the question to anybody who could be interested in it.
Thank you so much for your help.
I want to use VIC model for permafrost regions to simulate runoff under changing climatic and vegetation cover. Any guide / information may be very supportive and helpful for me. So please share if you have any kind of support / guide /help etc.
For my study on soil carbon content under different land uses in the Peruvian Amazon I am measuring bulk density. Mainly in the upper 10 cm from primary and secondary forest there are considerable amount of roots above 2x2 mm. I am correcting for stones by assuming stones to have a bulk density of 2.6 g/cm-3, but I have not been able to find literature on how to correct for roots?
I assume (dry) roots are somehow lighter than the soil they substitute, so in order to not underestimate BD of the forests, I need a conversion rate - although roots are naturally present here?
I am not measuring root biomass in my study.
Thanks in advance for your time!
I am currently researching the emission variations between agricultural land and energy crop cultivation. There is extensive literature about the argument of preserving agricultural land for food production and keeping energy crop production separate. This is primarily due to the increasing demand of food and decreasing available land for such agriculture.
My research has led me to the understanding that there are additional carbon emissions that need to be accounted for when calculating the environmental impacts of energy crops. This is due to previously stored carbon in the soils of uncultivated land, such as grassland, which is released once cultivation begins.
Is anyone working on or know of a study that has extensively calculated the emissions of both separate land uses and/or the variations between the same piece of land being used for agriculture and then converted into energy crops?
As part of a research work, I am modelling Land Use/Land cover changes over the span of 4 decades on a 40 km² watershed in a rural area located in sahelian climate (west africa). I am mainly interested in three types of land units, namely bare soils, cultivated soils and vegetated areas. I have been able to map them accurately enough using remote sensing analysis of Landsat 5 TM/Landsat 8 OLI Images. As such, I now have land cover maps for the watershed at four dates, 1985, 1995, 2007 and 2017. I am now looking into modelling the changes using a set of driving factors (yet to be identified) and assess land cover changes. I ran into land use simulation models mostly based on Cellular Automaton (CA) concepts such as Land Use Sim (http://www.landusesim.com/landusesim-land-use-modeling-simulation-software/) but it seems to be a paid software. Are there any other free simulation software/packages one can point me at ? Preferably, something that can be easily tight to a GIS environnment (ESRI ArcGIS for example) for easier raster/vector processing.
Thanks in advance.
in rapid urban expansion, peri-urban area's landscape (forest, hill area, wetlands) are occurred to change by uncontrolled land-use, impact on buffering settings. how to measure and make further policies to overcome?
I would like to meet any researcher/expert in this area from a German University of Research Institution?
Dear HYBAM scientific team,
My name is Fabian Santos, I am from Ecuador and currently I am doing a Ph.D. at the University of Bonn. Since data from my country is not easy to collect, I am wondering if you have a publication which summarizes the results of HYBAM project for the Napo Watershed, as my research is analysing its land cover/land use change during the last 40 years.
I will be pleased and thankful for your information as I didn't find any scientific publication of your results and this certainly, do not benefit knowledge transferability.
Thanks for your attention.
Can anyone recommend any advantages and disadvantages:
1) agent-based models;
2) artificial neural networks;
3) cellular automata;
4) economics-based models; and
5) Markov chains;
for land use change modelling?
Any sugestions, references?
What satellite images are the best for studying land-use changes in urban areas and why? Some free sources would be great, thanks.
Can anyone advise how to do a sensitivity analysis on the effect of land use change on river flow regimes? I want to find out which parameter/factor is contributing more effect to stream flow. The parameters are land use activities, i.e. cropped land, woodland, bare land, residential, water bodies and grassland. I only have information on streamflow and percentage land cover for each activity for different periods from 1980.
hello , I want to calculate the potential of different land use on co2 sequestration in my case study, but i cant find a citable paper that show me how much co2 sink in soil, forest and other land use generally. thanks for your help
The inventory for the CBM-CFS3 (carbon balance model from the canadian forest sector) requires volume-age curves for each stand and I cannot figure out how to obtain these curves.
I can find information about dbh, aboveground biomass and volume but I cannot find how this relates to age. Can anyone tell me what I am missing here? I am specifically looking at mexican oaks (Quercus castanea, Q. crassifolia, Q. laeta, Q. obtusata y Q. rugosa).
The model developers, Kurz et al. (2009) state: "Forest management agencies and industry have built up large libraries of yield tables to describe the accumulation of volume in the merchantable portion of tree stems as a function of stand age. To enable the use of these data sources, CBM-CFS2 was modified from using biomass over age to CBM-CFS3, that uses merchantable volume over age data to simulate growth."
Where can I find these tables with volume-age tables?
I am looking to analyse how a change in land use in a catchment will effect peak discharge for the floods in Carlisle 2015. What model would be suitable and what data would be needed to do this?